Systems and Methods for Disturbance Detection and Identification Based on Disturbance Analysis

ABSTRACT

Intelligent disturbance detection systems and methods of use to capture a disturbance via an application tool on a mobile smart device remote from the user, extract features from the disturbance, compare the extracted features to disturbance labels of a disturbance set in a comparison by a disturbance detection neural network model of the application tool, generate a disturbance label when the extracted features match the disturbance label in the comparison, train the model to generate a custom disturbance label associated with the extracted features when the extracted features do not match the one or more disturbance labels in the comparison, and generate an automatic alert via the mobile smart device to transmit an identification of the disturbance to the user based on the disturbance label, the custom disturbance label, or combinations thereof.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Pat. Application No.16/985,602, filed Aug. 5, 2020, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates to automated disturbance detection andidentification solutions and, in particular, systems and methods forautomated disturbance detection and disturbance identification based onintelligent disturbance analysis.

BACKGROUND

Older models of smart devices tend to be recycled or otherwise discardedupon a user purchasing a newer model. Such devices, however, are stillcapable of being used with their computational architecture fordifferent applications. Accordingly, a need exists for alternativesolutions to repurpose and utilize the older models of the smartdevices.

BRIEF SUMMARY

According to the subject matter of the present disclosure, anintelligent disturbance detection system may include a mobile smartdevice remote from a user, an application tool downloaded on the mobilesmart device, the application tool comprising a disturbance detectionneural network model and a disturbance set, the disturbance setcomprising one or more disturbance labels, one or more processorscommunicatively coupled to the application tool, one or more memorycomponents communicatively coupled to the one or more processors, andmachine readable instructions stored in the one or more memorycomponents. The machine readable instructions may cause the intelligentdisturbance detection system to perform at least the following whenexecuted by the one or more processors: capture a disturbance comprisinga sound, an image, or combinations thereof via the application tool onthe mobile smart device remote from the user, extract features from thedisturbance to generate one or more extracted features, compare the oneor more extracted features to the one or more disturbance labels in acomparison by the disturbance detection neural network model, andgenerate a disturbance label from the one or more disturbance labelswhen the one or more extracted features match the disturbance label inthe comparison. The machine readable instructions may further cause theintelligent disturbance detection system to perform at least thefollowing when executed by the one or more processors: train thedisturbance detection neural network model to generate a customdisturbance label associated with the one or more extracted featureswhen the one or more extracted features do not match the one or moredisturbance labels in the comparison, and generate an automatic alertvia the mobile smart device to transmit an identification of thedisturbance to the user based on the disturbance label, the customdisturbance label, or combinations thereof.

According to another embodiment of the present disclosure, a method ofimplementing an intelligent disturbance detection system may includecapturing a disturbance comprising a sound, an image, or combinationsthereof via an application tool on a mobile smart device of theintelligent disturbance detection system remote from a user, extractingfeatures from the disturbance to generate one or more extractedfeatures, comparing the one or more extracted features to one or moredisturbance labels of a disturbance set in a comparison by a disturbancedetection neural network model of the application tool, generating adisturbance label from the one or more disturbance labels when the oneor more extracted features match the disturbance label in thecomparison. The method may further include training the disturbancedetection neural network model to generate a custom disturbance labelassociated with the one or more extracted features when the one or moreextracted features do not match the one or more disturbance labels inthe comparison, and generating an automatic alert via the mobile smartdevice to transmit an identification of the disturbance to the userbased on the disturbance label, the custom disturbance label, orcombinations thereof.

According to yet another embodiment of the present disclosure, a methodof implementing an intelligent disturbance detection system may includecapturing a disturbance comprising a sound, an image, or combinationsthereof via an application tool on a mobile smart device of theintelligent disturbance detection system remote from a user, extractingfeatures from the disturbance to generate one or more extractedfeatures, comparing the one or more extracted features to one or moredisturbance labels of a disturbance set in a comparison by a disturbancedetection neural network model of the application tool, generating adisturbance label from the one or more disturbance labels when the oneor more extracted features match the disturbance label in thecomparison, and training the disturbance detection neural network modelto generate a custom disturbance label associated with the one or moreextracted features when the one or more extracted features do not matchthe one or more disturbance labels in the comparison. The method mayfurther include generating an automatic alert via the mobile smartdevice to transmit an identification of the disturbance to the userbased on the disturbance label, the custom disturbance label, orcombinations thereof, wherein the automatic alert comprises a timestampand a confidence level associated with the identification of thedisturbance.

Although the concepts of the present disclosure are described hereinwith primary reference to a disturbance detection solution of a homeenvironment, it is contemplated that the concepts will enjoyapplicability to any setting for purposes of disturbance detectionsolutions, such as alternative business settings or otherwise.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description of specific embodiments of thepresent disclosure can be best understood when read in conjunction withthe following drawings, where like structure is indicated with likereference numerals and in which:

FIG. 1 illustrates a control scheme of an intelligent disturbancedetection solution utilizing an artificial intelligence disturbancemodel including a machine learning functionality, according to one ormore embodiments shown and described herein;

FIG. 2 illustrates a schematic view of the intelligent disturbancedetection solution of FIG. 1 , according to one or more embodimentsshown and described herein;

FIG. 3A illustrates a computer implemented system having aself-contained architecture, the system including an intelligentdisturbance detection module for use with the process flows describedherein and the intelligent disturbance detection solution of FIGS. 1-2 ,along with a screen view for an alert generated by the intelligentdisturbance detection module, according to one or more embodiments shownand described herein;

FIG. 3B illustrates a smart device sub-system of the self-containedarchitecture system of FIG. 3A;

FIG. 3C illustrates an application tool sub-system of the self-containedarchitecture system of FIG. 3A;

FIG. 3D illustrates another computer implemented system having anexternal communication architecture broader than the self-containedarchitecture of FIG. 3A and configured to operate with the smart deviceand application tool sub-systems of FIGS. 3B-3C, according to one ormore embodiments shown and described herein;

FIG. 4 illustrates a screen view of a mobile smart device utilized for adisturbance detection and generated by the intelligent disturbancedetection module of FIG. 3 , according to one or more embodiments shownand described herein; and

FIG. 5 illustrates a flowchart process for use of the intelligentdisturbance detection solution of FIGS. 1-2 and the intelligentdisturbance detection module and system of FIG. 3 , according to one ormore embodiments shown and described herein.

DETAILED DESCRIPTION

In embodiments described herein and in greater detail below, anintelligent disturbance detection module includes machine learningfunctionality to implement systems and methods to generate a disturbanceidentification and automated alert regarding the disturbanceidentification. The embodiments herein are directed to a functionalityfor a mobile smart device to employ an artificial intelligence model onan application tool for disturbance detection by the mobile smartdevice. By way of example, and not as a limitation, a mobile smartdevice that a user no longer carries may stay in a home environment todetect sounds or images of disturbances via the application tool asdescribed herein. The artificial intelligence model may capture adisturbance, such as a sound or image, extract features from the soundor image, match the extracted features to a model trained data set,apply a type of disturbance label from the match, and generate an alertto a user, such as via text or email, based on the type of disturbancelabel to notify the user of the disturbance. As non-limiting examples,such sounds may be a dog barking, a fire alarm, or a doorbell ringing.Signals of disturbances may be processed and features extracted by theapplication tool as described herein, which may be based on computerprogramming languages such as Python or other suitable programminglanguages as understood by those skilled in the art and may be designedusing Java, Android Studio, or other suitable design environments asunderstood by those skilled in the art. Although the disturbances aredescribed herein as a sound or image, it is within the scope of thepresent disclosure and should be understood that such disturbances mayalso be a combination of one or more sounds, a combination of one ormore images, or combinations thereof.

In some embodiments, a confidence value may be applied to the matchprior to generating the alert such that only alerts above a confidencethreshold are transmitted. Additionally or alternatively, the confidencelevel associated with the generated alert may be transmitted. Further, auser may customize selection of which labels are to generate alerts tothe user. Moreover, a user may train the model to recognize and labelcustomized sounds. For example, such a customized sound may be of a dooropening. Further, the application tool may be set to monitor for thedetected disturbances at predetermined time periods. In someembodiments, an insurance company system may be coupled to a pluralityof application tools in a predetermined area to receive the alerts andgenerate metrics to determine parameters such as a safety in an areabased on a number of alerts of one or more types of disturbance labelsreceived.

Referring to FIG. 1 , an intelligent disturbance detection solution 100includes one or more training file disturbance data sets 102, one ormore trained file disturbance input labels 104, artificial intelligencedisturbance algorithm 106, a machine learning disturbance detectionmodel 108, monitored disturbance data 110, and one or more generateddisturbance labels 120.

The machine learning disturbance detection model 108 utilizes theartificial intelligence disturbance algorithm 106 to generate the one ormore generated disturbance labels 120 associated with the monitoreddisturbance data 110. The machine learning disturbance detection model108 and the artificial intelligence disturbance algorithm 106 aretrained using the one or more training file disturbance data sets 102that are associated during training with the one or more trained filedisturbance input labels 104. Thus, during training, a disturbance asinput from the one or more training file disturbance data sets 102 isassociated with a trained filed disturbance input label 104 to classifythe disturbance as a labeled and identified disturbance that is detectedand generated as a generated disturbance label 120.

Additionally, the machine learning disturbance detection model 108 isconfigured to utilize one or more disturbances from the monitoreddisturbance data 110 that is not associated with the one or more trainedfile disturbance input labels 104 to generate one or more associatedcustom disturbance labels to add to the one or more trained filedisturbance input labels 104.

FIG. 2 illustrates a sub-level embodiment of the intelligent disturbancedetection solution 100 of FIG. 1 including a plurality of trainingsamples 102A, 102B, 102C, and 102D as the one or more training filedisturbance data sets 102. The disturbances may include captured sounds,images, or combinations thereof. The plurality of training samples 102A,102B, 102C, and 102D include, for example, audio waveforms andcorresponding audio spectrograms from which features 112A, 112B, 112C,and 112D are respectively extracted. While described in terms of audiowaveforms and audio spectrograms, it should be understood that trainingsamples 102 and/or test sample 110 may take a variety of other forms.The extracted features 112A, 112B, 112C, and 112D, correspond to labels114A, 114B, 114C, 114D such that the artificial intelligence disturbancealgorithm 106 is trained to recognize labels 114 to associate withextracted features 112. By way of example, and not as a limitation, theextracted features 112A, 112B, 112C, and 112D may respectivelycorrespond in FIG. 2 to labels of glass breaking, a dog barking, afaucet dripping, and a fire alarm (and/or a carbon monoxide alarm). Theone or more generated disturbance labels 120 may be associated with theextracted features 112A, 112B, 112C, and 112D to identify and label atype of disturbance having an associated disturbance label with eachextracted feature. The artificial intelligence disturbance algorithm 106is trained utilizing the one or more generated disturbance labels 120 tolearn to determine whether a new monitored disturbance as the monitoreddisturbance data 110 corresponds to one of the one or more generateddisturbance labels 120.

The machine learning disturbance detection model 108 utilizes thetrained artificial intelligence disturbance algorithm 106 to generatethe one or more generated disturbance labels 120 to identify one or moredisturbance inputs as the monitored disturbance data 110. As shown inFIG. 2 , a test sample as a disturbance input as the monitoreddisturbance data 110 includes an associated audio waveform and audiospectrogram from which features 116 are extracted. To determine a label118 to associate with the disturbance input of the monitored disturbancedata 110, the machine learning disturbance detection model 108 appliesthe trained artificial intelligence disturbance algorithm 106 togenerate the generated disturbance label 120 associated with themonitored disturbance data 110. The machine learning disturbancedetection model 108 may apply the trained artificial intelligencedisturbance algorithm 106 and not find a match to generate the generateddisturbance label 120 associated with the monitored disturbance data110. In such embodiments, the artificial intelligence disturbancealgorithm 106 may be further trained to input the monitored disturbancedata 110 as a customized disturbance to associated with a customizeddisturbance label.

FIG. 3A illustrates a computer implemented intelligent disturbancedetection system 200 having a self-contained architecture for use withthe processes described herein, such as a process 300 of FIG. 5 , asdescribed in greater detail below. FIG. 3B, which will be described ingreater detail further below, illustrates a smart device sub-system ofthe mobile smart device 250 to operate within the system 200 of FIG. 3A.FIG. 3C, also described in greater detail below, illustrates anapplication tool-subsystem to operate within the system 200 of FIG. 3A.FIG. 3D illustrates another computer implemented system 200 having anexternal communication architecture broader than the self-containedarchitecture of FIG. 3A and is similar to the system 200 of FIG. 3A asdescribed herein with differences such as a server 220 and computingdevice 224 remote from the mobile smart device 250 noted in greaterdetail with respect to FIG. 3D below. The smart device and applicationtool sub-systems of FIGS. 3B and 3C are configured to also operate withrespect to the system 200 of FIG. 3D. In the system embodimentsdescribed herein, the systems may include one or more variations of (1)a closed system associated with and self-contained to a single deviceincluding an application tool as described herein in an environment,such as a house, (2) a closed system associated with and self-containedto multiple devices sharing a common user account associated with theapplication tool 226 in the environment, (3) a closed system associatedwith and self-contained to multiple devices under a plurality of sharedor linked user accounts, such as between family members, associated withthe application tool 226 in the environment, (4) an open system in whichthe application tool 226 may share predicted events but not customizedlabels, or (5) an open system in which the application tool 226 mayshare predicted events and/or customized labels with a big dataarchitecture external the application tool 226. In the open systems, theapplication tool 226 may communicate to a central server 220, such asthe central server 220 of FIG. 3D, to receive and transmit, for example,software updates, backup trained sounds, sharing of trained data with afamily member, additional computing power, and/or other suitable systemdata.

Referring to FIG. 3A, with respect to one or more self-contained closedsystem embodiments, a non-transitory, intelligent disturbance detectionsystem 200 for implementing a computer and software-based method, suchas directed by the intelligent disturbance detection solution 100 andthe processes described herein, to automatically generate the one ormore generated disturbance labels 120 to identify monitored and detecteddisturbances as described herein. The intelligent disturbance detectionsystem 200 comprises an intelligent disturbance detection module 201A ofthe self-contained architecture as a component of the machine learningdisturbance detection model 108 of FIG. 1 to generate the one or moregenerated disturbance labels 120. With respect to the self-containedarchitecture of the intelligence disturbance detection module 201A ofFIG. 3A, once an application tool 226 is downloaded to the mobile smartdevice 250 through a network 222, the mobile smart device 250 isconfigured to operate the application tool 226 as described hereinwithout any outside communicative connections to external communicationdevices or system.

The intelligent disturbance detection system 200 further comprises acommunication path 202, one or more processors 204, a non-transitorymemory component 206, a disturbance capture module 208 to capture amonitored disturbance from monitored disturbance data 110, andisturbance detection analytics module 212, a disturbance detectiontraining model module 212A of the disturbance detection analytics module212, a storage or database 214, a machine learning module 216, a networkinterface hardware 218, and a network 222, and an application tool 226as an “app” downloaded on or otherwise communicatively coupled to themobile smart device 250. In some embodiments, the intelligentdisturbance detection system 200 is implemented using a wide areanetwork (WAN) or network 222, such as an intranet or the internet andthe application tool 226 is downloaded via the network 222. Oncedownloaded on a mobile smart device 250, the disturbance detection model108 is run on the mobile smart device 250 via the application tool 226and may be configured to receive monitored disturbance data 110 andcreate new labels 120 unique to an environment in which the mobile smartdevice 250 is disposed. By way of example, and not as a limitation, theenvironment may be a home environment, and the new label 120 may be asound associated with a creaky door in the home environment that soundsdifferent from a creaky door in a training sample. The variouscomponents of the intelligent disturbance detection system 200 and theinteraction thereof will be described in detail below. The disturbancecapture module 208 is configured to receive one or more disturbances asthe monitored disturbance data 110. The monitored disturbance data 110may be initially be captured and transmitted through a camera 232 and/ormicrophones 234 on the mobile smart device 250 as shown in FIG. 3B. As anon-limiting example, the mobile smart device 250 may be a smartphoneincluding the application tool 226 configured to use the machinelearning disturbance detection model 108 to generate the one or moregenerated disturbance labels 120 as described herein.

As shown in FIG. 3A, the mobile smart device 250 may capture adisturbance as monitored disturbance data 110 associated with a waveform254 that is analyzed by the machine learning disturbance detection model108 through analysis 256 to generate a disturbance label 260 associatedwith the disturbance, such as an image and/or disturbance type, and anassociated confidence level 258. In FIG. 3A, the disturbance isdetermined in a determination to be dripping water from a faucet asshown by the disturbance label 260 with a confidence level 258 of 93%associated with the determination. An alert 262 is further generatedassociated with the determination of the detected and identifieddisturbance. In FIG. 3A, the dripping faucet disturbance is determinedto have occurred at 12:29 p.m., and a See Activity feature may beavailable to select to learn further information about the disturbancedetection and determination.

FIG. 4 illustrates a screen view of a mobile smart device 250 utilizedfor a disturbance detection and generated by the intelligent disturbancedetection analytics module 212 and intelligent disturbance detectionsystem 200 of FIG. 3A or FIG. 3D. The screen view shows a display graph270 of detected disturbances 272 and an associated timestamp. The screenview further shows a selection bar 274 including icons to select andconfigured to direct an individual selecting the icon to differentcomponents of the application tool 226, such as a monitor componentconfigured to start monitoring the disturbance data 110. In theembodiment of FIG. 4 , the detected disturbances are shown as fire/smokealarm, no alert (i.e., no disturbance detected), and dog with associatedconfidence levels of the disturbance detection determinations shown inthe display graph 270.

The intelligent disturbance detection system 200 comprises thecommunication path 202. The communication path 202 may be formed fromany medium that is capable of transmitting a signal such as, forexample, conductive wires, conductive traces, optical waveguides, or thelike, or from a combination of mediums capable of transmitting signals.The communication path 202 communicatively couples the variouscomponents of the intelligent disturbance detection system 200. As usedherein, the term “communicatively coupled” means that coupled componentsare capable of exchanging data signals with one another such as, forexample, electrical signals via conductive medium, electromagneticsignals via air, optical signals via optical waveguides, and the like.

The intelligent disturbance detection system 200 of FIG. 3A alsocomprises the processor 204. The processor 204 can be any device capableof executing machine readable instructions. Accordingly, the processor204 may be a controller, an integrated circuit, a microchip, a computer,or any other computing device. The processor 204 is communicativelycoupled to the other components of the intelligent disturbance detectionsystem 200 by the communication path 202. Accordingly, the communicationpath 202 may communicatively couple any number of processors with oneanother, and allow the modules coupled to the communication path 202 tooperate in a distributed computing environment. Specifically, each ofthe modules can operate as a node that may send and/or receive data.

The illustrated system 200 further comprises the memory component 206which is coupled to the communication path 202 and communicativelycoupled to the processor 204. The memory component 206 may be anon-transitory computer readable medium or non-transitory computerreadable memory and may be configured as a nonvolatile computer readablemedium. The memory component 206 may comprise RAM, ROM, flash memories,hard drives, or any device capable of storing machine readableinstructions such that the machine readable instructions can be accessedand executed by the processor 204. The machine readable instructions maycomprise logic or algorithm(s) written in any programming language suchas, for example, machine language that may be directly executed by theprocessor, or assembly language, object-oriented programming (OOP),scripting languages, microcode, etc., that may be compiled or assembledinto machine readable instructions and stored on the memory component206. Alternatively, the machine readable instructions may be written ina hardware description language (HDL), such as logic implemented viaeither a field-programmable gate array (FPGA) configuration or anapplication-specific integrated circuit (ASIC), or their equivalents.Accordingly, the methods described herein may be implemented in anyconventional computer programming language, as pre-programmed hardwareelements, or as a combination of hardware and software components.

Still referring to FIG. 3A, as noted above, the intelligent disturbancedetection system 200 comprises the display such as a graphical userinterface (GUI) on a screen of the computing device 224 for providingvisual output such as, for example, information, graphical reports,messages, or a combination thereof. The display on the screen of themobile smart device 250 is coupled via an internal communication path202 to the processor 204 of the mobile smart device 250. Accordingly,the communication path 202 communicatively couples the display to othermodules of the intelligent disturbance detection system 200. The displaycan comprise any medium capable of transmitting an optical output suchas, for example, a cathode ray tube, light emitting diodes, a liquidcrystal display, a plasma display, or the like. Additionally, it isnoted that the display or the computing device 224 can comprise at leastone of the processor 204 and the memory component 206.

The intelligent disturbance detection system 200 comprises thedisturbance detection analytics module 212 as described above to atleast apply data analytics and artificial intelligence algorithms andmodels to received disturbances, such as sounds, videos, images, orcombinations thereof, and the machine learning module 216 for providingsuch artificial intelligence algorithms and models. The machine learningmodule 216 may include an artificial intelligence component toautomatically, and after the disturbance detection analytics module 212is implemented via the application tool 226, train and provide machinelearning capabilities via machine learning techniques to a neuralnetwork as described herein.

By way of example, and not as a limitation, the neural network mayutilize one or more artificial neural networks (ANNs). In ANNs,connections between nodes may form a directed acyclic graph (DAG). ANNsmay include node inputs, one or more hidden activation layers, and nodeoutputs, and may be utilized with activation functions in the one ormore hidden activation layers such as a linear function, a stepfunction, logistic (sigmoid) function, a tanh function, a rectifiedlinear unit (ReLu) function, or combinations thereof. ANNs are trainedby applying such activation functions to training data sets to determinean optimized solution from adjustable weights and biases applied tonodes within the hidden activation layers to generate one or moreoutputs as the optimized solution with a minimized error. In machinelearning applications, new inputs may be provided (such as the generatedone or more outputs) to the ANN model as training data to continue toimprove accuracy and minimize error of the ANN model. The one or moreANN models may utilize one to one, one to many, many to one, and/or manyto many (e.g., sequence to sequence) sequence modeling. The intelligentdisturbance detection system 200 may utilize one or more ANN models asunderstood to those skilled in the art or as yet-to-be-developed togenerate disturbance labels and alerts as described in embodimentsherein. Such ANN models may include artificial intelligence componentsselected from the group that may include, but not be limited to, anartificial intelligence engine, Bayesian inference engine, and adecision-making engine, and may have an adaptive learning engine furthercomprising a deep neural network learning engine. The one or more ANNmodels may employ a combination of artificial intelligence techniques,such as, but not limited to, Deep Learning, Random Forest Classifiers,Feature extraction from audio, images, clustering algorithms, orcombinations thereof.

In embodiments, a convolutional neural network (CNN) may be utilized.For example, a convolutional neural network (CNN) may be used as an ANNthat, in a field of machine learning, for example, is a class of deep,feed-forward ANNs applied for audio-visual analysis of the captureddisturbances. CNNs may be shift or space invariant and utilizeshared-weight architecture and translation invariance characteristics.Additionally or alternatively, a recurrent neural network (RNN) may beused as an ANN that is a feedback neural network. RNNs may use aninternal memory state to process variable length sequences of inputs togenerate one or more outputs. In RNNs, connections between nodes mayform a DAG along a temporal sequence. One or more different types ofRNNs may be used such as a standard RNN, a Long Short Term Memory (LSTM)RNN architecture, and/or a Gated Recurrent Unit RNN architecture.

The disturbance detection analytics module 212, the disturbancedetection training model module 212A, and the machine learning module216 are coupled to the communication path 202 and communicativelycoupled to the processor 204. As will be described in further detailbelow, the processor 204 may process the input signals received from thesystem modules and/or extract information from such signals.

Data stored and manipulated in the intelligent disturbance detectionsystem 200 as described herein is utilized by the machine learningmodule 216, which in embodiments able to leverage a cloudcomputing-based network configuration such as the cloud to apply machinelearning and artificial intelligence or may be able to rely on aninternal architecture of the application tool 226 to apply machinelearning and artificial intelligence as described herein. This machinelearning application may create models that can be applied by theintelligent machine learning and artificial intelligence system 200, tomake it more efficient and intelligent in execution. As an example andnot a limitation, the machine learning module 216 may include artificialintelligence components selected from the group consisting of anartificial intelligence engine, Bayesian inference engine, and adecision-making engine, and may have an adaptive learning engine furthercomprising a deep neural network learning engine.

The intelligent disturbance detection system 200 comprises the networkinterface hardware 218 for communicatively coupling the intelligentdisturbance detection system 200 with a computer network such as network222. The network interface hardware 218 is coupled to the communicationpath 202 such that the communication path 202 communicatively couplesthe network interface hardware 218 to other modules of the intelligentdisturbance detection system 200. The network interface hardware 218 canbe any device capable of transmitting and/or receiving data via awireless network. Accordingly, the network interface hardware 218 cancomprise a communication transceiver for sending and/or receiving dataaccording to any wireless communication standard. For example, thenetwork interface hardware 218 can comprise a chipset (e.g., antenna,processors, machine readable instructions, etc.) to communicate overwired and/or wireless computer networks such as, for example, wirelessfidelity (Wi-Fi), WiMax, Bluetooth, IrDA, Wireless USB, Z-Wave, ZigBee,or the like.

Referring to FIG. 3B, a smart device sub-system of the mobile smartdevice 250 is configured to operate within the system 200. As shown inFIG. 3B, the mobile smart device 250 may include a processor 204, amemory component 206, the application tool 226, a display device 228, auser input device 230, a camera 232, and a microphone 234. The displaydevice 228 may be a display of the mobile smart device 250, and the userinput device 230 may be a graphical user interface of the mobile smartdevice 250. The camera 232 and the microphone 232 may be communicativelycoupled to and configure to operate with the disturbance capture module208.

Referring to FIG. 3C, an application tool-subsystem is configured tooperate within the system 200. As shown in FIG. 3C, the application tool226 may include a pre-processing module 236, a feature extraction module238, a feature mapping module 240, a predict event module 242, thedisturbance detection analytics module 212, a persistence layer 244, aconfiguration module 246, and an alerting module 248. The pre-processingmodule 236 may be communicatively coupled to and configured to operatewith the disturbance capture module 208. The feature extraction module238, the feature mapping module 240, the predict event module 242, thepersistence layer 244, the configuration module 246, and the alertingmodule 248 are communicatively coupled to and configured to operate ascomponents of the disturbance detection analytics module 212 of theapplication tool 226. The persistence layer 244 may be a communicativelycoupled to and a component of the database 214 described herein. Inembodiments, the persistence layer 244 may store event data of monitoreddisturbances as described herein, and the configuration module 246 maycontrol configuration of settings associated with the application tool226.

As noted above, FIG. 3D illustrates another computer implemented system200 having an external communication architecture broader than theself-contained architecture of FIG. 3A and is directed to one or moreopen system embodiments. The system 200 of FIG. 3D is similar to thesystem 200 of FIG. 3A as described herein with differences such as aserver 220 and a computing device 224 remote from the mobile smartdevice 250. Further, the intelligent disturbance detection system 200 ofFIG. 3D comprises an intelligent disturbance detection module 201B ofthe external communication architecture as a component of the machinelearning disturbance detection model 108 of FIG. 1 to generate the oneor more generated disturbance labels 120. With respect to the externalcommunication architecture of the intelligence disturbance detectionmodule 201B of FIG. 3D, the application tool 226 may be downloaded tothe mobile smart device 250 and/or the computing device 224 through anetwork 222, and the mobile smart device 250, the computing device 224,and the server 220 may be communicatively coupled to share informationprovided by and with respect to the application tool 226. The smartdevice and application tool sub-systems of FIGS. 3B and 3C areconfigured to also operate with respect to the system 200 of FIG. 3D.

Referring to FIG. 3D, data from various applications running oncomputing device 224 can be provided from the computing device 224 tothe intelligent disturbance detection system 200 via the networkinterface hardware 218. The computing device 224 can be any devicehaving hardware (e.g., chipsets, processors, memory, etc.) forcommunicatively coupling with the network interface hardware 218 and anetwork 222. Specifically, the computing device 224 can comprise aninput device having an antenna for communicating over one or more of thewireless computer networks described above.

While only one server 220 and one computing device 224 is illustrated,the intelligent disturbance detection system 200 can comprise multipleservers containing one or more applications and computing devices. Thecomputing device 224 may include digital systems and other devicespermitting connection to and navigation of the network. It iscontemplated and within the scope of this disclosure that the computingdevice 224 may be a personal computer, a laptop device, a mobile smartdevice such as a smartphone or smart pad or tablet, or the like. Otherintelligent disturbance detection system 200 variations allowing forcommunication between various geographically diverse components arepossible. The lines depicted in FIG. 2 indicate communication ratherthan physical connections between the various components.

The network 222 can comprise any wired and/or wireless network such as,for example, wide area networks, metropolitan area networks, theinternet, an intranet, satellite networks, or the like. Accordingly, thenetwork 222 can be utilized as a wireless access point by the computingdevice 224 to access one or more servers (e.g., a server 220). Theserver 220 and any additional servers generally comprise processors,memory, and chipset for delivering resources via the network 222.Resources can include providing, for example, processing, storage,software, and information from the server 220 to the intelligentdisturbance detection system 200 via the network 222. Additionally, itis noted that the server 220 and any additional servers can shareresources with one another over the network 222 such as, for example,via the wired portion of the network, the wireless portion of thenetwork, or combinations thereof. While the intelligent disturbancedetection system 200 is illustrated as a single, integrated system inFIG. 3B, in other embodiments, the systems can be independent systems.

In embodiments, the machine learning disturbance detection model 108 ofFIG. 1 may be communicatively to a “big data” environment including adatabase 214 of the intelligent disturbance detection module 201B of theexternal communication architecture of FIG. 3D configured to store andprocess large volumes of data in such an environment. The applicationtool 226 may be configured to be communicatively coupled to the database214 of such a “big data” environment such that the application tool 226may communicate with one or more external devices, systems, orapplication tools across technical platforms. The database 214 may be,for example, a structured query language (SQL) database or a likedatabase that may be associated with a relational database managementsystem (RDBMS) and/or an object-relational database management system(ORDBMS). The database 214 may be any other large-scale storage andretrieval mechanism whether a SQL, SQL including, or a non-SQL database.For example, the database 214 may utilize one or more big data storagecomputer architecture solutions. Such big data storage solutions maysupport large data sets in a hyperscale and/or distributed computingenvironment, which may, for example, include a variety of serversutilizing direct-attached storage (DAS). Such database environments mayinclude Hadoop, NoSQL, and Cassandra that may be usable as analyticsengines. Thus, while SQL may be referenced herein as an example databasethat is used with the tool described herein, it is understood that anyother such type of database capable of support large amounts ofdatabase, whether currently available or yet-to-be developed, and asunderstood to those of ordinary skill in the art, may be utilized withthe tool described herein as well.

Referring to FIG. 5 , a process 300 is shown for use with thedisturbance detection training model module 212A and the disturbancedetection analytics module 212 and the intelligent disturbance detectionsystem 200 of FIGS. 3A or 3D to generate the one or more generateddisturbance labels 120 determinations based on disturbance detectionanalysis as described herein. In embodiments, the intelligentdisturbance detection system 200 may include the mobile smart device 250remote from a user, and an application tool 226 downloaded on the mobilesmart device 250. The application tool 226 may include a disturbancedetection neural network model, such as the machine learning disturbancedetection model 108, and a disturbance set. The disturbance set mayinclude one or more disturbance labels, such as the one or more trainedfile disturbance input labels 104 of FIG. 1 . Referring to FIG. 3A, theintelligent disturbance detection system 200 may include the processor204 of the mobile smart device 250 communicatively coupled to theapplication tool 226, the memory component 206 of the mobile smartdevice 250, and machine readable instructions stored in the memorycomponent 206 that cause the intelligent disturbance detection system200 to perform a control scheme or process as described herein, such asthe intelligent disturbance detection solution 100 and/or the process300, when executed by the processor 204 of the mobile smart device 250.Referring to FIG. 3D, the intelligent disturbance detection system 200may further include one or more processors 204 communicatively coupledto the application tool 226, one or more memory components 206communicatively coupled to the one or more processors 204, and machinereadable instructions stored in the one or more memory components 206that cause the intelligent disturbance detection system 200 to perform acontrol scheme or process as described herein, such as the intelligentdisturbance detection solution 100 and/or the process 300, when executedby the one or more processors 204.

The machine readable instructions may cause the intelligent disturbancedetection system 200 when executed by the one or more processors 204 tocapture a disturbance, such as through the monitored disturbance data110, comprising a sound, an image, or combinations thereof via theapplication tool 226 on the mobile smart device 250 remote from theuser, and extract features from the disturbance to generate one or moreextracted features. As a non-limiting example, the process 300 includesa block 302 to capture a disturbance via the mobile smart device 250even when the mobile smart device 250 is remote from the user. By way ofexample, and not as a limitation, the mobile smart device 250 may beconfigured to use the application tool 226 as described herein tomonitor an environment, such as a home environment, when the user isfrom home. In embodiments, the disturbance may be a sound and/or imagefrom which features are extracted in block 304. The pre-processingmodule 236 of the disturbance capture module 208 of the application tool226 may be utilized to capture and process disturbance information asdescribed herein.

In block 306, the extracted features are matched to a trained data setof a machine learning disturbance detection model 108 and/or train themachine learning disturbance detection model 108 to recognize and labelcustomized disturbances, such as sounds, by the intelligent disturbancedetection system 200 as described herein. The feature extraction module236 of the disturbance detection analytics module 212 of the applicationtool 226 may be utilized to extract features from the captureddisturbance information as described herein.

In block 308, a type of disturbance label is determined and appliedbased on the match of the block 306. The feature mapping module 240 ofthe disturbance detection analytics module 212 of the application tool226 may be utilized to map extracted features to stored matchingdisturbance labels as described herein. The predict event module 242 ofthe disturbance detection analytics module 212 of the application tool226 may then be utilized to predict the monitored event based on thematching disturbance label as described herein. As a non-limitingexample, in some embodiments, the machine readable instructions maycause the intelligent disturbance detection system 200 when executed bythe one or more processors 204 to compare the one or more extractedfeatures to the one or more disturbance labels (e.g., the one or moretrained file disturbance input labels 104 of FIG. 1 ) in a comparison bythe disturbance detection neural network model (e.g., the machinelearning disturbance detection model 108 of FIG. 1 ), generate adisturbance label 120 from the one or more disturbance labels when theone or more extracted features match the disturbance label in thecomparison, and train the disturbance detection neural network model togenerate a custom disturbance label 120 associated with the one or moreextracted features 116 when the one or more extracted features 116 donot match the one or more disturbance labels (e.g., of the one or moretrained file disturbance input labels 104 of FIG. 1 ) in the comparison.In embodiments, the disturbance label 120 may include an identificationof one of a dog barking sound, a fire alarm sound, or a doorbell ringingsound, and the custom disturbance label 120 may include anidentification of a door opening sound.

In some embodiments, the machine readable instructions may cause theintelligent disturbance detection system 200 when executed by the one ormore processors 204 to transmit instructions to add the customdisturbance label 120 to the disturbance set based on an approval of theuser, which may include a user setting of the custom disturbance label120. The user setting may be directed to a naming of the customdisturbance label, an upload by the user of an image for the customdisturbance label, or combinations thereof. In embodiments, the machinereadable instructions may cause the intelligent disturbance detectionsystem 200 when executed by the one or more processors 204 to upload animage associated with the custom disturbance label 120 and add thecustom disturbance label 120 to the disturbance set (e.g., the one ormore trained file disturbance input labels 104 of FIG. 1 ).

In block 310 of the process 300 of FIG. 5 , an alert 262 is generated asdescribed herein and transmitted to a user based on the type ofdisturbance label 120 determined and generated. The alerting module 248of the application tool 226 may be utilized to extract one or morepatterns from the captured disturbance information as described herein.The alert 262 may be, for example, sent to a text or an email as set bythe user in the application tool 226. The user may set the text via aninput phone number and/or one or more email addresses to which to sendthe alerts 262, may set a recording frequency as described in greaterdetail below, and may select a feature on the application tool 226 tostart monitoring for monitored disturbance data 110 to generate the oneor more alerts 262 per the recording frequency set. The application 226may generate an image or sound clip to send as the alert 262corresponding to a generated disturbance label 120 as described herein.In some embodiments, the application tool 226 may generate one or moresnapshot screen views to send as the alert 262 corresponding to thegenerated disturbance label 120 as described herein. In embodiments, asound clip or other disturbance clip associated with the disturbance ofthe monitored disturbance data 110 corresponding to the alert 262 may besent as part of the alert 262 for the user to review. As a non-limitingexample, the sound clip or other disturbance clip can be attached forreview with the one or more snapshot screen views sent to the user asthe alert 262.

In some embodiments, a user may customize selection of which types ofgenerated disturbance labels 120 from the monitored disturbance data 110are to generate alerts 262 to send to the user. The user may add orremove disturbance labels 120 already stored in the intelligentdisturbance detection system 200 (e.g., as the one or more trained filedisturbance input labels 104 of FIG. 1 ) for which alerts 262 may besent. Thus, the user may customize which alerts 262 the user desires toreceive and at what frequency the user wishes to receive the selectedalerts 262.

In some embodiments, the machine readable instructions may cause theintelligent disturbance detection system 200 when executed by the one ormore processors 204 to generate an automatic alert 262 via the mobilesmart device 250 to transmit an identification of the disturbance to theuser based on the disturbance label 120, the custom disturbance label120, or combinations thereof. The automatic alert 262 may include a textto the user, an email to the user, or combinations thereof. The user mayset up information associated with the text and the email to which tosend the notification in the application tool 226. The application tool226 may be configured to transmit the automatic alert to a second deviceof the user at which, for example, the user may receive the automaticalert 262 as the text, the email, or combinations thereof. Inembodiments, the automatic alert 262 may include a timestamp associatedwith the identification of the disturbance, a confidence levelassociated with the identification of the disturbance, or combinationsthereof. The automatic alert 262 may include a display graph 270 over aperiod of time, which may include at least one disturbance time portionassociated with the identification of the disturbance. The display graph270 may include at least one time portion not associated with theidentification of the disturbance. In embodiments, the automatic alert262 may include a display graph 270 over a period of time that includesat least one disturbance time portion associated with a disturbancedetection including the identification of the disturbance,identification of another disturbance from the disturbance set, orcombinations thereof, and the display graph 270 over the period of timemay include at least one time portion not associated with thedisturbance detection.

The user may also set a recording frequency in the application tool 226.The recording frequency may include a predetermined time period in whichto monitor the monitored disturbance data 110 by the application tool226 of the mobile smart device 250. The application tool 226 may beconfigured to generate the automatic alert based on a frequencyassociated with the automatic alert 262. The frequency may include anumber of times to send the automatic alert 262, a time period withinwhich to send the automatic alert as one or more alerts 262, a timeperiod between each subsequent automatic alert 262 of the automaticalert 262, or combinations thereof. In embodiments, a number of alerts262 may be set for a particular generated disturbance label 120 and/or anumber of iterations in a time period may be set for the particulargenerated disturbance label 120. As a non-limiting example, an alert 262of a fire alarm may be set to be sent to the user via text and/or emailtwo times. Additionally or alternatively, the one or more alerts 262 ofthe first alarm may be sent in iterations such as every 15 minutes untilthe alert 262 is cleared or otherwise handled. The application tool 226may include a feature configured to clear an alert 262.

In embodiments, the intelligent disturbance detection solution systemsand methods as described herein assist to significantly reduceinefficiencies associated with disturbance detection by efficientlyhandling disturbance detection determinations in a first instance toresult in faster disturbance classification and identification, forexample. As a non-limiting example, such disturbances may be receivedvia application tools 226. The intelligent disturbance detectionsolution systems and methods provide a more efficient and customizableprocessing system to efficiently and automatically handle disturbancedetection determinations, effectively reducing a use of processing powerwhile optimizing system usage and efficiencies, while further allowingfor a use of mobile smart devices 250 that may be older models no longercarried by a user and utilized as a primary smart device.

In some embodiments, the intelligent disturbance detection solutionsystems and methods may be directed to an application tool 226downloaded on a mobile smart device 250 that does not store themonitored disturbance data 110 from a monitoring session. The monitoreddisturbance data 110 from the monitoring session may be received by theapplication 226 and analyzed in slices of a continuous monitoring duringthe monitoring session to determine a primary disturbance, such as aloudest sound, from which to extract features. The extracted featuresfrom the primary disturbance are used by the machine learningdisturbance detection model 108 to generate the generated disturbancelabels 120 as described herein. A secondary disturbance in a slice maybecome a new primary disturbance in another slice such that theapplication tool 226 is configured to extract features from the newprimary disturbance during the monitoring session during the continuousmonitoring.

In some embodiments as described herein, the intelligent disturbancedetection solution systems and methods may be directed to an applicationtool 226 downloaded on a mobile smart device 250 that is not paired withother devices such that data in kept private and stored local in themobile smart device 250 and alerts 262 are generated via text and/oremail, such as through transmission by a home wireless fidelity (wi-fi)network to which the mobile smart device 250 is connected andcommunicatively coupled. In alternative embodiments, the intelligentdisturbance detection solution systems and methods described herein mayinclude an application tool 226 configured to be downloaded on multiplepaired smart devices. The application tool 226 on the mobile smartdevice 250 may be configured to recognize and approve one or more smartdevices for pairing and sharing of data by the application tool 226,such as one or more smart devices found on the same home wi-fi networkto which the mobile smart device 250 is connected. In some embodiments,different mobile smart devices 250 may include respectively downloadeddifferent application tools 226 that are not in communication with anddo not share data with one or another. In some embodiments, creation ofa custom disturbance label 120 on a first application tool 226 on afirst mobile smart device 250 may not cause a corresponding creation ofthe custom disturbance label 120 in a second application tool 226 on asecond mobile smart device 250, even in the same home wi-fi network. Inother embodiments, creation of a custom disturbance label 120 on thefirst application tool 226 on the first mobile smart device 250 maycause a corresponding creation of the custom disturbance label 120 inthe second application tool 226 on the second mobile smart device 250 ifboth the first and second mobile smart devices 250 are communicativelycoupled via a common user account associated with both the first andsecond application tools 226.

In some embodiments as described herein, the intelligent disturbancedetection solution systems and methods may be directed to an applicationtool 226 downloaded on a mobile smart device 250 that is static and doesnot access external networks such as cloud-based servers. In alternativeembodiments, the intelligent disturbance detection solution systems andmethods described herein may include an application tool 226 configuredto dynamically access external networks and/or be operational betweenpaired mobile smart devices 250.

In other embodiments as described herein, the intelligent disturbancedetection solution systems and methods may be directed to an applicationtool 226 downloaded on a mobile smart device 250 that is configured tosend third party security alerts and/or monitored disturbance data 110information. By way of example, and not as a limitation, alerts 262 maybe sent to a security agency monitoring a home, to other securityenforcement agencies, and/or to an insurance agency. The insuranceagency may insure the home being monitored by the application tool 226,for instance. Alternatively or additionally, the insurance agency mayuse an insurance company system coupled to a plurality of applicationtools 226 in a predetermined area to receive the respective alerts 262from monitored disturbance data 110 information, such as from multiplehomes, to generate metrics. The metrics may be used to determineparameters such as a safety in the area parameter based on a number ofalerts 262 received of one or more types of generated disturbance labels120.

For the purposes of describing and defining the present disclosure, itis noted that reference herein to a variable being a “function” of aparameter or another variable is not intended to denote that thevariable is exclusively a function of the listed parameter or variable.Rather, reference herein to a variable that is a “function” of a listedparameter is intended to be open ended such that the variable may be afunction of a single parameter or a plurality of parameters.

It is also noted that recitations herein of “at least one” component,element, etc., should not be used to create an inference that thealternative use of the articles “a” or “an” should be limited to asingle component, element, etc.

It is noted that recitations herein of a component of the presentdisclosure being “configured” or “programmed” in a particular way, toembody a particular property, or to function in a particular manner, arestructural recitations, as opposed to recitations of intended use.

It is noted that terms like “preferably,” “commonly,” and “typically,”when utilized herein, are not utilized to limit the scope of the claimeddisclosure or to imply that certain features are critical, essential, oreven important to the structure or function of the claimed disclosure.Rather, these terms are merely intended to identify particular aspectsof an embodiment of the present disclosure or to emphasize alternativeor additional features that may or may not be utilized in a particularembodiment of the present disclosure.

Having described the subject matter of the present disclosure in detailand by reference to specific embodiments thereof, it is noted that thevarious details disclosed herein should not be taken to imply that thesedetails relate to elements that are essential components of the variousembodiments described herein, even in cases where a particular elementis illustrated in each of the drawings that accompany the presentdescription. Further, it will be apparent that modifications andvariations are possible without departing from the scope of the presentdisclosure, including, but not limited to, embodiments defined in theappended claims. More specifically, although some aspects of the presentdisclosure are identified herein as preferred or particularlyadvantageous, it is contemplated that the present disclosure is notnecessarily limited to these aspects.

It is noted that one or more of the following claims utilize the term“wherein” as a transitional phrase. For the purposes of defining thepresent disclosure, it is noted that this term is introduced in theclaims as an open-ended transitional phrase that is used to introduce arecitation of a series of characteristics of the structure and should beinterpreted in like manner as the more commonly used open-ended preambleterm “comprising.”

Aspects Listing

Aspect 1. An intelligent disturbance detection system comprising amobile smart device remote from a user, an application tool downloadedon the mobile smart device, one or more processors communicativelycoupled to the application tool, one or more memory componentscommunicatively coupled to the one or more processors, and machinereadable instructions stored in the one or more memory components. Theapplication tool comprises a disturbance detection neural network modeland a disturbance set, and the disturbance set comprises one or moredisturbance labels. The machine readable instructions cause theintelligent disturbance detection system to perform at least thefollowing when executed by the one or more processors: capture adisturbance comprising a sound, an image, or combinations thereof viathe application tool on the mobile smart device remote from the user,extract features from the disturbance to generate one or more extractedfeatures, compare the one or more extracted features to the one or moredisturbance labels in a comparison by the disturbance detection neuralnetwork model, and generate a disturbance label from the one or moredisturbance labels when the one or more extracted features match thedisturbance label in the comparison. The machine readable instructionsfurther cause the intelligent disturbance detection system to perform atleast the following when executed by the one or more processors: trainthe disturbance detection neural network model to generate a customdisturbance label associated with the one or more extracted featureswhen the one or more extracted features do not match the one or moredisturbance labels in the comparison, and generate an automatic alertvia the mobile smart device to transmit an identification of thedisturbance to the user based on the disturbance label, the customdisturbance label, or combinations thereof.

Aspect 2. The intelligent disturbance detection system of Aspect 1,wherein the automatic alert comprises a text to the user, an email tothe user, or combinations thereof.

Aspect 3. The intelligent disturbance detection system of Aspect 1 orAspect 2, further comprising machine readable instructions that causethe intelligent disturbance detection system to perform at least thefollowing when executed by the one or more processors: transmit theautomatic alert to a second device of the user.

Aspect 4. The intelligent disturbance detection system of any of Aspect1 to Aspect 3, further comprising machine readable instructions thatcause the intelligent disturbance detection system to perform at leastthe following when executed by the one or more processors: generate theautomatic alert based on a frequency associated with the automaticalert, the frequency comprising a number of times to send the automaticalert, a time period within which to send the automatic alert as one ormore alerts, a time period between each subsequent automatic alert ofthe automatic alert, or combinations thereof.

Aspect 5. The intelligent disturbance detection system of any of Aspect1 to Aspect 4, wherein the automatic alert comprises a timestampassociated with the identification of the disturbance.

Aspect 6. The intelligent disturbance detection system of any of Aspect1 to Aspect 5, wherein the automatic alert comprises a confidence levelassociated with the identification of the disturbance.

Aspect 7. The intelligent disturbance detection system of any of Aspect1 to Aspect 6, wherein the automatic alert comprises a display graphover a period of time, the display graph over the period of timecomprising at least one disturbance time portion associated with theidentification of the disturbance.

Aspect 8. The intelligent disturbance detection system of Aspect 7,wherein the display graph comprises at least one time portion notassociated with the identification of the disturbance.

Aspect 9. The intelligent disturbance detection system of any of Aspect1 to Aspect 8, wherein the automatic alert comprises a display graphover a period of time, the display graph over the period of timecomprising at least one disturbance time portion associated with adisturbance detection, the disturbance detection comprising theidentification of the disturbance, an identification of anotherdisturbance from the disturbance set, or combinations thereof.

Aspect 10. The intelligent disturbance detection system of Aspect 9,wherein the display graph over the period of time comprises at least onetime portion not associated with the disturbance detection.

Aspect 11. The intelligent disturbance detection system of any of Aspect1 to Aspect 10, wherein the disturbance label comprises theidentification of one of a dog barking sound, a fire alarm sound, or adoorbell ringing sound.

Aspect 12. The intelligent disturbance detection system of any of Aspect1 to Aspect 11, wherein the custom disturbance label comprises theidentification of a door opening sound.

Aspect 13. The intelligent disturbance detection system of any of Aspect1 to Aspect 12, wherein the application tool is configured to transmitinstructions to add the custom disturbance label to the disturbance setbased on an approval of the user, the approval of the user comprising auser setting of the custom disturbance label.

Aspect 14. The intelligent disturbance detection system of Aspect 13,wherein the user setting comprises a naming of the custom disturbancelabel, an upload by the user of an image for the custom disturbancelabel, or combinations thereof.

Aspect 15. The intelligent disturbance detection system of any of Aspect1 to Aspect 14, further comprising machine readable instructions thatcause the intelligent disturbance detection system to perform at leastthe following when executed by the one or more processors: upload animage associated with the custom disturbance label, and add the customdisturbance label to the disturbance set.

Aspect 16. A method of implementing an intelligent disturbance detectionsystem, the method comprising capturing a disturbance comprising asound, an image, or combinations thereof via an application tool on amobile smart device of the intelligent disturbance detection systemremote from a user, extracting features from the disturbance to generateone or more extracted features, comparing the one or more extractedfeatures to one or more disturbance labels of a disturbance set in acomparison by a disturbance detection neural network model of theapplication tool, and generating a disturbance label from the one ormore disturbance labels when the one or more extracted features matchthe disturbance label in the comparison. The method further comprisestraining the disturbance detection neural network model to generate acustom disturbance label associated with the one or more extractedfeatures when the one or more extracted features do not match the one ormore disturbance labels in the comparison, and generating an automaticalert via the mobile smart device to transmit an identification of thedisturbance to the user based on the disturbance label, the customdisturbance label, or combinations thereof.

Aspect 17. The method of Aspect 16, further comprising generating theautomatic alert based on a frequency associated with the automaticalert, the frequency comprising a number of times to send the automaticalert as one or more alerts, a time period within which to send theautomatic alert as the one or more alerts, a time period between eachsubsequent automatic alert of the automatic alert, or combinationsthereof.

Aspect 18. The method of Aspect 17, further comprising setting by theuser a name of the custom disturbance label during an approval of theuser, uploading by the user an image for the custom disturbance labelduring the approval of the user, and adding the custom disturbance labelto the disturbance set based on the approval of the user.

Aspect 19. A method of implementing an intelligent disturbance detectionsystem, the method comprising capturing a disturbance comprising asound, an image, or combinations thereof via an application tool on amobile smart device of the intelligent disturbance detection systemremote from a user, extracting features from the disturbance to generateone or more extracted features, comparing the one or more extractedfeatures to one or more disturbance labels of a disturbance set in acomparison by a disturbance detection neural network model of theapplication tool, generating a disturbance label from the one or moredisturbance labels when the one or more extracted features match thedisturbance label in the comparison, training the disturbance detectionneural network model to generate a custom disturbance label associatedwith the one or more extracted features when the one or more extractedfeatures do not match the one or more disturbance labels in thecomparison, and generating an automatic alert via the mobile smartdevice to transmit an identification of the disturbance to the userbased on the disturbance label, the custom disturbance label, orcombinations thereof, wherein the automatic alert comprises a timestampand a confidence level associated with the identification of thedisturbance.

Aspect 20. The method of Aspect 19, wherein the automatic alert furthercomprises a display graph over a period of time, the display graph overthe period of time comprising at least one disturbance time portionassociated with the identification of the disturbance.

What is claimed is:
 1. An intelligent disturbance detection systemcomprising: an application tool executed by a mobile smart device, theapplication tool comprising a disturbance detection neural network modeland a disturbance set, the disturbance set comprising one or moredisturbance labels; one or more processors communicatively coupled tothe application tool; one or more memory components communicativelycoupled to the one or more processors; and machine readable instructionsstored in the one or more memory components that cause the intelligentdisturbance detection system to perform at least the following whenexecuted by the one or more processors: capture a disturbance comprisinga sound, an image, or combinations thereof via the application tool onthe mobile smart device remote from a user; extract features from thedisturbance to generate one or more extracted features; compare the oneor more extracted features to the one or more disturbance labels in acomparison by the disturbance detection neural network model; generate adisturbance label from the one or more disturbance labels when the oneor more extracted features match the disturbance label in thecomparison; train the disturbance detection neural network model togenerate a new disturbance label for the disturbance set associated withthe one or more extracted features when the one or more extractedfeatures do not match the one or more disturbance labels in thecomparison; and generate an automatic alert via the mobile smart deviceto transmit an identification of the disturbance to the user based onthe disturbance label, the new disturbance label, or combinationsthereof, wherein the automatic alert comprises a display graph over aperiod of time, the display graph over the period of time comprising atleast one disturbance time portion associated with the identification ofthe disturbance.
 2. The intelligent disturbance detection system ofclaim 1, wherein the automatic alert comprises a text to the user, anemail to the user, or combinations thereof.
 3. The intelligentdisturbance detection system of claim 1, further comprising machinereadable instructions that cause the intelligent disturbance detectionsystem to perform at least the following when executed by the one ormore processors: transmit the automatic alert to a second device of theuser.
 4. The intelligent disturbance detection system of claim 1,further comprising machine readable instructions that cause theintelligent disturbance detection system to perform at least thefollowing when executed by the one or more processors: generate theautomatic alert based on a frequency associated with the automaticalert, the frequency comprising a number of times to send the automaticalert, a time period within which to send the automatic alert as one ormore alerts, a time period between each subsequent automatic alert ofthe automatic alert, or combinations thereof.
 5. The intelligentdisturbance detection system of claim 1, wherein the automatic alertcomprises a timestamp associated with the identification of thedisturbance.
 6. The intelligent disturbance detection system of claim 1,wherein the automatic alert comprises a confidence level associated withthe identification of the disturbance.
 7. The intelligent disturbancedetection system of claim 1, wherein the display graph comprises atleast one time portion not associated with the identification of thedisturbance.
 8. The intelligent disturbance detection system of claim 1,wherein the at least one disturbance time portion is associated with adisturbance detection, the disturbance detection comprising theidentification of the disturbance, an identification of anotherdisturbance from the disturbance set, or combinations thereof.
 9. Theintelligent disturbance detection system of claim 8, wherein the displaygraph over the period of time comprises at least one time portion notassociated with the disturbance detection.
 10. The intelligentdisturbance detection system of claim 1, wherein the disturbance labelcomprises the identification of one of a dog barking sound, a fire alarmsound, or a doorbell ringing sound.
 11. The intelligent disturbancedetection system of claim 1, wherein the new disturbance label comprisesthe identification of a door opening sound.
 12. The intelligentdisturbance detection system of claim 1, wherein the application tool isconfigured to transmit instructions to add the new disturbance label tothe disturbance set based on an approval of the user, the approval ofthe user comprising a user setting of the new disturbance label.
 13. Theintelligent disturbance detection system of claim 12, wherein the usersetting comprises a naming of the new disturbance label, an upload bythe user of an image for the new disturbance label, or combinationsthereof.
 14. The intelligent disturbance detection system of claim 1,further comprising machine readable instructions that cause theintelligent disturbance detection system to perform at least thefollowing when executed by the one or more processors: upload an imageassociated with the new disturbance label; and add the new disturbancelabel to the disturbance set.
 15. A method of implementing anintelligent disturbance detection system, the method comprising:capturing a disturbance comprising a sound, an image, or combinationsthereof via an application tool on a mobile smart device of theintelligent disturbance detection system remote from a user; extractingfeatures from the disturbance to generate one or more extractedfeatures; comparing the one or more extracted features to one or moredisturbance labels of a disturbance set in a comparison by a disturbancedetection neural network model of the application tool; generating adisturbance label from the one or more disturbance labels when the oneor more extracted features match the disturbance label in thecomparison; training the disturbance detection neural network model togenerate a new disturbance label for the disturbance set associated withthe one or more extracted features when the one or more extractedfeatures do not match the one or more disturbance labels in thecomparison; and generating an automatic alert via the mobile smartdevice to transmit an identification of the disturbance to the userbased on the disturbance label, the new disturbance label, orcombinations thereof, wherein the automatic alert comprises a displaygraph over a period of time, the display graph over the period of timecomprising at least one disturbance time portion associated with theidentification of the disturbance.
 16. The method of claim 15, furthercomprising: generating the automatic alert based on a frequencyassociated with the automatic alert, the frequency comprising a numberof times to send the automatic alert as one or more alerts, a timeperiod within which to send the automatic alert as the one or morealerts, a time period between each subsequent automatic alert of theautomatic alert, or combinations thereof.
 17. The method of claim 16,further comprising: setting by the user a name of the new disturbancelabel during an approval of the user; uploading by the user an image forthe new disturbance label during the approval of the user; and addingthe new disturbance label to the disturbance set based on the approvalof the user.
 18. A method of implementing an intelligent disturbancedetection system, the method comprising: capturing a disturbancecomprising a sound, an image, or combinations thereof via an applicationtool on a mobile smart device of the intelligent disturbance detectionsystem remote from a user; extracting features from the disturbance togenerate one or more extracted features; comparing the one or moreextracted features to one or more disturbance labels of a disturbanceset in a comparison by a disturbance detection neural network model ofthe application tool; generating a disturbance label from the one ormore disturbance labels when the one or more extracted features matchthe disturbance label in the comparison; training the disturbancedetection neural network model to generate a new disturbance label forthe disturbance set associated with the one or more extracted featureswhen the one or more extracted features do not match the one or moredisturbance labels in the comparison; generating an automatic alert viathe mobile smart device to transmit an identification of the disturbanceto the user based on the disturbance label, the new disturbance label,or combinations thereof, wherein the automatic alert comprises atimestamp and a confidence level associated with the identification ofthe disturbance; and generating the automatic alert based on a frequencyassociated with the automatic alert, the frequency comprising a numberof times to send the automatic alert, a time period within which to sendthe automatic alert as one or more alerts, a time period between eachsubsequent automatic alert of the automatic alert, or combinationsthereof.
 19. The method of claim 18, wherein the automatic alert furthercomprises a display graph over a period of time, the display graph overthe period of time comprising at least one disturbance time portionassociated with the identification of the disturbance.